Data Engineer - ETL/Snowflake DB

FirstHive | CDP+AI Data Platform; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

FirstHive | CDP+AI Data Platform in Bengaluru is seeking a Data Engineer to build platform-level data infrastructure powering the CDP across client connectors, CDC ingestion, and transformation services.

You will architect ingestion frameworks, CDC pipelines, schema inference, and quality tooling to ensure trustworthy data for dashboards and analytics. Strong Java/Spring Boot, SQL, and streaming stack experience required.

Qualifications

• 4+ years building data systems in production
• Production-grade Java and Spring Boot
• SQL fluency with data modeling depth
• Depth with streaming and data warehouses

Responsibilities

• Build platform-level data infrastructure for the CDP
• Develop ingestion frameworks over Kafka/Kafka Connect
• Implement CDC ingestion pipelines and schema handling
• Create transformation and data quality tooling
• Design data models for StarRocks, Snowflake, BigQuery
• Maintain metadata, configuration-driven transformations

Skills

SQL

Java

Spring Boot

Tools

Kafka

Kafka Connect

Debezium

Airflow

Argo Workflows

dbt

MongoDB

StarRocks

Snowflake

BigQuery

Kubernetes

Job description

About The Role

We're hiring a Data Engineer in Bangalore to build the platform-level data infrastructure that powers FirstHive's Customer Data Platform across every client integration connector frameworks, CDC ingestion pipelines, transformation services, and data quality tooling.

Designation :

Data Engineer

Location :

Bengaluru

About The Role

We're hiring a Data Engineer in Bangalore to build the platform-level data infrastructure that powers FirstHive's Customer Data Platform across every client integration connector frameworks, CDC ingestion pipelines, transformation services, and data quality tooling.

What You'll Build

Organized by where it sits in the pipeline. Within each group, the most architectural work comes first.

Ingestion frameworks the first thing every new client hits :

• Pluggable connector framework over Kafka and Kafka Connect for databases, APIs, file feeds, and event streams custom SMTs, DLQ patterns, reusable connector configurations.
• CDC ingestion pipelines from MongoDB and relational sources via Debezium and Kafka Connect multi-database routing, schema change handling, ordering guarantees.
• Automated schema mapping, detection, and inference tooling so onboarding a new client is configuration, not engineering.

Transformation And Quality What Makes The Data Trustworthy

• Transformation layer as composable Spring Boot modules: cleaning, deduplication, normalization, identity resolution, enrichment. Not glue scripts.
• Data quality framework profiling, validation gates, anomaly detection, lineage tracking wired into the pipeline so bad data is caught at ingestion, not at the dashboard.
• Schema evolution handling backward compatibility across Kafka topics, transformations, and warehouse tables when client source schemas change.

Warehouse Layer Where It Lands And Gets Queried

• Data models for StarRocks, Snowflake, and BigQuery partitioning, clustering / bucketing, materialization strategy, primary-key vs. duplicate vs. aggregate table design.
• Optimized SQL and stored procedures for mixed workloads: point lookups, high-concurrency customer profile dashboards, and large batch ETL.
• Metadata layer driving per-client schema definitions, mapping rules, and transformation logic controlled by configuration, not code changes.

What We Need

• Grouped by where it matters. The first bullet of each group is the non-negotiable.

4+ years building data systems not running them :

• You've designed and shipped framework-level data systems in production. You can point to ones still running.

Production-grade Java And Spring Boot

• Real microservices: error handling, observability, testing, lifecycle management. Not scripts.
• Framework-builder instinct reusable tooling for the next ten clients, not the next ticket.

SQL Fluency And Data Modeling Depth

• Complex joins, window functions, CTEs (including recursive), and a real instinct for performance and cost.
• Star schema, SCD types, event sourcing, EAV patterns and judgment on when each is the right answer.

Real Depth On The Streaming And Warehouse Stack

• Kafka and Kafka Connect at depth: connector configuration, custom transforms and converters, consumer group design, DLQ patterns, exactly-once vs. at-least-once tradeoffs.
• At least one analytical warehouse at architecture level StarRocks, Snowflake, or BigQuery covering data modeling, performance tuning, partitioning / clustering, and cost optimization.
• MongoDB or similar document store schema design, compound indexing, change streams, CDC tradeoffs.
• Workflow orchestration in production Airflow, Argo Workflows, dbt, or similar.

Bonus, Not Gating

• These don't decide the hire, but they shape the shortlist :
• Debezium at production scale buffer / lock tuning, multi-database capture, snapshot strategies.
• StarRocks, ClickHouse, Druid, or similar MPP / OLAP engines.
• Open table formats Apache Iceberg, Hudi and lakehouse architectures.
• Multi-cloud Kubernetes (GKE, EKS) and object storage (GCS, S3).
• Identity resolution deterministic / probabilistic matching, graph-based stitching.
• CDP, MarTech, or AdTech domain exposure.

(ref:hirist.tech)